Field of the Invention
The present invention relates to an image region segmentation apparatus and method.
Description of the Related Art
A region segmentation method is used as a technique for segmenting an image into a plurality of regions so attributes such as color, pattern, brightness or the like, of respective regions become the same. After the regions are thus segmented, various image processing can be performed in units of regions, and therefore it is possible to reduce a computation amount compared to a case where an image is processed in units of pixels.
Several methods for accelerating region segmentation processing have been proposed. In these, a method for dividing an image into a plurality of regions by clustering pixel data using 5-dimensional information having color information (l, a, b) and coordinates (X, Y) is disclosed in “R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC Superpixels,” tech. rep., EPFL, EPFL, 2010.” (document 1). The method of document 1 is referred to as Simple Linear Iterative Clustering (SLIC). First of all, representative data which are the center of clusters are allocated in a reticular pattern in an image. The representative data in the SLIC method holds 5-dimensional information having color information (l, a, b) and coordinates (X, Y). Note that the representative data is also referred to as a seed or a cluster centroid. The SLIC method is based on a k-means method which is one type of clustering, and clusters pixel data for representative data allocated in a reticular pattern. Also, using 5-dimensional information of the clustered pixel data, each representative data item is updated and clustered once again. In the SLIC method, a region segmentation result is obtained by repeating such clustering and representative data updating one or more times. A difference between the SLIC method and the k-means method is in the point that the range of pixel data that is clustered to representative data is limited to a predetermined range. A region obtained by clustering is referred to as a superpixel.
Also, accelerating the method of document 1 by a Graphic Processing Unit (GPU) is disclosed in “C. Y. Ren and I. Reid. gSLIC: a real-time implementation of SLIC superpixel segmentation. University of Oxford, Department of Engineering, Technical Report, 2011.” (document 2). In document 2, a hierarchical clustering scheme for realizing high speed processing by a GPU has been proposed, and high speed region segmentation processing for a high-resolution image is realized.
Also, in “T. Maruyama, “Real-time K-Means Clustering for Color Images on Reconfigurable Hardware”, International Conference on Pattern Recognition, pp. 816-819, 2006” (document 3), it is disclosed that a representative data initialization method is contrived and k-means processing is accelerated. In document 3, it is proposed that when a plurality of similar images are processed in order, by performing acceleration by processing the first image as normal, and using the representative data obtained in the previous image from the second image on, a repetition count is reduced.
However, if the method of acceleration as in document 3 is applied to a moving image, when a large change in the image occurs such as when the camera is moved or when there is a scene change, a phenomenon in which the pixel data is not clustered to any of the representative data occurs. Such a phenomenon is referred to as a dead cluster, a dead centroid, an empty cluster, or the like (hereinafter referred to as a dead cluster in the present specification). When a dead cluster occurs, the number of regions is reduced, and the precision of the region segmentation is degraded.